超轻量视觉惯性里程计实现抗噪测试时自适应，适合嵌入式部署。
UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation
- 压缩模型至100万参数以下，保留批量归一化参数以支持轻量适配。
- 在KITTI上仅增加1%误差，相比顶尖方法缩小36倍模型体积。
- 利用惯性输出作伪标签动态调整参数，首次实现噪声鲁棒的测试时适应。
基于数据驱动的视觉惯性里程计（VIO）因其性能优异受到广泛关注，但其在资源受限设备上的部署面临挑战，因大模型参数需占用大量内存。此外，模型在部署后可能因测试环境分布变化而失效。为此，我们提出UL-VIO——一个参数少于100万的超轻量级VIO网络，具备基于视觉-惯性一致性的测试时自适应（TTA）能力。通过模型压缩保留低层编码器及全部批归一化参数，实现资源高效适配。在保持误差仅增加1%的前提下，模型规模比现有最优方案缩小36倍（在KITTI数据集上）。针对测试时适应，我们采用惯性引导的网络输出作为伪标签，更新批归一化参数，实现轻量且有效的自适应。据我们所知，这是首个在VIO中实现噪声鲁棒测试时自适应的工作。在KITTI、EuRoC和Marulan数据集上的实验表明，该方法在多种具有动态域偏移的测试场景下均有效。
原文摘要 · Abstract (English)
Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networks may risk failure post-deployment due to environmental distribution shifts at test time. In light of this, we propose UL-VIO -- an ultra-lightweight (<1M) VIO network capable of test-time adaptation (TTA) based on visual-inertial consistency. Specifically, we perform model compression to the network while preserving the low-level encoder part, including all BatchNorm parameters for resource-efficient test-time adaptation. It achieves 36X smaller network size than state-of-the-art with a minute increase in error -- 1% on the KITTI dataset. For test-time adaptation, we propose to use the inertia-referred network outputs as pseudo labels and update the BatchNorm parameter for lightweight yet effective adaptation. To the best of our knowledge, this is the first work to perform noise-robust TTA on VIO. Experimental results on the KITTI, EuRoC, and Marulan datasets demonstrate the effectiveness of our resource-efficient adaptation method under diverse TTA scenarios with dynamic domain shifts.
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